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Bayesian Statistics Engineer Jobs (NOW HIRING)

MORSE Corp

Bayesian Data Scientist

Cambridge, MA · On-site

$90K - $210K/yr

MORSE is searching for an experienced Data Scientist with expertise in Bayesian statistics ... D in Data Science, Statistics, Computer Science, Engineering, Applied Mathematics, Physics ...

ROSALIND

Bioinformatics Engineer

San Diego, CA · On-site

As a Bioinformatics Engineer, you will develop software for analyzing and visualizing complex ... Bayesian statistics and multivariate analysis • Experience with version control systems (e.g ...

Wood Plc

Environmental Statistician

Kennesaw, GA · On-site +1

S. in statistics, mathematics, or a science or engineering field * Well-versed in R, MATLAB, or ... Experience with Bayesian statistics a plus * Experience with censored datasets a plus * Master ...

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Bayesian Statistics Engineer information

What does a Bayesian statistics engineer do?

A Bayesian Statistics Engineer applies Bayesian statistical methods to analyze data, build predictive models, and solve complex problems across various fields such as finance, healthcare, and technology. They focus on incorporating prior knowledge and updating probabilities as new data becomes available. Their work often involves designing experiments, implementing probabilistic models using software tools, and communicating findings to stakeholders. Additionally, they collaborate with data scientists and engineers to develop scalable statistical solutions.

What are the key skills and qualifications needed to thrive as a Bayesian statistics engineer?

To thrive as a Bayesian Statistics Engineer, you need a strong background in probability theory, statistical modeling, and Bayesian inference, often supported by an advanced degree in statistics, mathematics, or a related field. Proficiency with programming languages such as Python or R, along with experience using probabilistic programming tools like Stan or PyMC, is typically required. Critical thinking, problem-solving abilities, and effective communication skills help in translating complex statistical concepts to non-experts and collaborating across teams. These competencies are essential for developing accurate predictive models and ensuring data-driven decisions in complex real-world applications.

What are some common challenges faced by Bayesian statistics engineers when integrating Bayesian models into production systems?

Bayesian Statistics Engineers often encounter challenges related to computational efficiency and scalability when deploying probabilistic models in real-world applications. Ensuring that models run efficiently with large-scale or streaming data can require advanced sampling techniques or approximate inference methods. Additionally, translating complex statistical outputs into actionable insights for stakeholders and collaborating with software engineers to maintain robust, reproducible pipelines are key aspects of the role. Effective communication and a strong understanding of both statistical theory and software engineering best practices are essential for success.

What is the difference between Bayesian Statistics Engineer vs Data Scientist?

AspectBayesian Statistics EngineerData Scientist
Required CredentialsStatistics, Data Science, or related degrees; knowledge of Bayesian methodsStatistics, Data Science, Computer Science degrees; broad skill set including machine learning
Work EnvironmentResearch-focused, analytical teams, often in tech or financeCross-functional teams, product-focused, in various industries
Employer & Industry UsageTech companies, finance, healthcare with emphasis on probabilistic modelingWide range of industries including tech, marketing, healthcare, finance
Common Search & ComparisonSpecialized in Bayesian methods, probabilistic modelingBroader data analysis, machine learning, and visualization skills

While Bayesian Statistics Engineers focus on probabilistic modeling using Bayesian methods, Data Scientists have a broader scope including machine learning, data analysis, and visualization. Both roles require strong statistical knowledge, but Bayesian Statistics Engineers specialize in Bayesian techniques for complex modeling tasks.

What are popular job titles related to Bayesian Statistics Engineer jobs?

For Bayesian Statistics Engineer jobs, the most frequently searched job titles are:

Infographic showing various Bayesian Statistics Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 87% Full Time, 9% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Member of Technical Staff, Bayesian Statistics

New York, NY • On-site

Ataraxis AI
See more jobs at Ataraxis AIexternal

$100K - $300K/yr

Full-time

Re-posted 3 days ago

Key responsibilities

  • Design and implement novel Bayesian statistics methods.

  • Translate machine learning papers into production-ready code.

  • Build robust model evaluation frameworks.

Job description

About Ataraxis AI
Ataraxis is a clinical AI research lab working at the intersection of multi-modal AI and precision medicine. Our goal is to make disease predictable. To accomplish this, we develop new AI methods that predict patient outcomes and treatment response, and build clinical tools to assist physicians in selecting the most optimal treatments for their patients.
Our AI research lab discovers and develops methods to recognize patterns and predict outcomes across complex, multi-modal clinical data. This spans our causality (Ataraxis™ Tau), foundation model (Falcon and Kestrel for digital pathology), and survival analysis research.
Our first clinical products, such as Ataraxis™ Breast for breast cancer, already help patients get the most appropriate treatment across the best academic institutions and community clinics worldwide.
At Ataraxis, you will have a unique opportunity to shape not only the future of our company, but also the future of healthcare. You will join an exceptional team at the forefront of clinical AI research and deployment. Our advisors include AI pioneers such as our founding advisor, Yann LeCun, and distinguished oncologists from top cancer research institutions, all united by the mission to redefine precision medicine.
Ataraxis has raised over $24 million in funding, including a $20 million Series A led by top venture capital funds such as Thiel Capital/Founders Fund (OpenAI, SpaceX, Palantir), Obvious Ventures (AMI Labs, Inceptive, Radical Numerics, Recursion), and AIX Ventures (Hugging Face, Perplexity).
We are an company with a flat organizational structure, where every team member is empowered to actively contribute. Leadership roles are earned by those who demonstrate initiative and consistently deliver exceptional results. Strong work ethic and the ability to prioritize ruthlessly are essential.
Responsibilities
  • Design and implement novel Bayesian statistics methods.
  • Translate machine learning papers into production-ready code.
  • Build robust model evaluation frameworks.
  • Disseminate the results by co-authoring research papers and abstracts.
  • Collaborate with a multidisciplinary team of engineers and scientists.
  • Co-mentor junior members of the team.
Qualifications
  • PhD degree in statistics or machine learning.
  • Excellent knowledge of Bayesian statistics, including Gaussian processes and Bayesian clinical trial design.
  • Passion for research, attention to detail and ability to drive tasks to completion. Strong preference will be given to candidates with papers in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR) or top-tier statistics journals.
  • Excellent understanding of core machine learning concepts.
  • Excellent knowledge of the foundations of statistics, linear algebra, probability and machine learning.
  • Excellent skills in Python and PyTorch.
  • Experience with applying Bayesian statistics to uncertainty quantification in deep learning and model explainability.
  • Experience in deep learning. Experience in self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference, model interpretability and computational pathology is a bonus.

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